数据驱动的移动健康:系统识别和混合模型预测控制,以提供个性化的身体活动干预

Mohamed El Mistiri;Owais Khan;César A. Martin;Eric Hekler;Daniel E. Rivera
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引用次数: 0

摘要

行为医学中控制系统原则的整合涉及开发可个性化的干预措施,以促进健康行为,例如有意义和持续的体育活动。本文采用控制优化试验(COT)框架,将系统辨识和混合模型预测控制应用于个性化行为干预设计。本文详细介绍了COT的多个阶段,从系统识别的实验设计到控制器的实施,并利用Just Walk的参与者数据证明了其有效性,Just Walk是一种促进久坐成年人步行行为的干预措施。估计和验证数据的混合分区应用于估计说明性参与者的ARX模型,选择具有最佳性能的模型,超过加权规范平衡预测能力和整体数据拟合。该模型作为基于卡尔曼滤波的三自由度混合模型预测控制器(3DoF-KF HMPC)的内部模型,为身体活动干预的启动和维持阶段提供“雄心勃勃但可行”的目标。通过标称和蒙特卡罗仿真对闭环设置的性能和鲁棒性进行了评估;后者证实了控制器在植物模型不匹配下的固有鲁棒性。这些结果作为COT方法的概念证明,目前正在YourMove临床试验(R01CA244777, NCT05598996)中对人类参与者进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Driven Mobile Health: System Identification and Hybrid Model Predictive Control to Deliver Personalized Physical Activity Interventions
The integration of control systems principles in behavioral medicine involves developing interventions that can be personalized to foster healthy behaviors, such as meaningful and consistent engagement in physical activity. In this paper, system identification and hybrid model predictive control are applied to design individualized behavioral interventions using the control optimization trial (COT) framework. The paper details the multiple stages of a COT, from experimental design in system identification to controller implementation, and demonstrates its efficacy using participant data from Just Walk, an intervention that promotes walking behavior in sedentary adults. Mixed partitioning of estimation and validation data is applied to estimate ARX models for an illustrative participant, selecting the model with the best performance over a weighted norm balancing predictive ability with overall data fit. This model serves as the internal model in a three-degree-of-freedom Kalman filter-based Hybrid Model Predictive Controller (3DoF-KF HMPC) that provides “ambitious but doable” goals for initiation and maintenance phases of the physical activity intervention. Performance and robustness in a closed-loop setting are evaluated via both nominal and Monte Carlo simulation; the latter confirms the inherent robustness properties of the controller under plant-model mismatch. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove (R01CA244777, NCT05598996).
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